Background Modeling

This code performs background modeling and foreground estimation in dynamic
scenes captured by static cameras. The algorithm implemented has three
innovations over existing approaches. First, the correlation in intensities of
spatially proximal pixels is exploited by using a nonparametric density
estimation method over a joint domain-range representation of image pixels,
multimodal spatial uncertainties and complex dependencies between the domain
(location) and range (color). The model of the background is implemented as a
single probability density, as opposed to individual, independent, pixel-wise
distributions.

Second, temporal persistence is used as a detection criterion. Unlike previous
approaches to object detection which detect objects by building adaptive
models of the background, the foreground is modeled to augment the detection
of objects (without explicit tracking) since objects detected in the preceding
frame contain substantial evidence for detection in the current frame.

Finally, the background and foreground models are used competitively in a
MAP-MRF decision framework, stressing spatial context as a condition of
detecting interesting objects and the posterior function is maximized
efficiently by finding the minimum cut of a capacitated graph.

This method is useful for moving object detection in scenes containing dynamic
backgrounds, e.g., fountains, fans, and moving trees, etc. The entry point for
background modeling is Main.m.